SoroushVahidi/scidocs huggingface dataset:
A Hypercube-Based Indirect Encoding for Evolving Large-Scale Neural Networks
(2009)

Research in neuroevolution, i.e. evolving artificial neural networks (ANNs) through evolutionary algorithms, is inspired by the evolution of biological brains. Because natural evolution discovered intelligent brains with billions of neurons and trillions of connections, perhaps neuroevolution can do the same. Yet while neuroevolution has produced successful results in a variety of domains, the scale of natural brains remains far beyond reach. This paper presents a method called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective Compositional Pattern Producing Networks (connective CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. The advantage of this approach is that it can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution . HyperNEAT is demonstrated through visual discrimination and food gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution.
Answer Selection in Community Question Answering via Attentive Neural Networks
(2017)

Answer selection in community question answering (cQA) is a challenging task in natural language processing. The difficulty lies in that it not only needs the consideration of semantic matching between question answer pairs but also requires a serious modeling of contextual factors. In this letter, we propose an attentive deep neural network architecture so as to learn the deterministic information for answer selection. The architecture can support various input formats through the organization of convolutional neural networks, attention-based long short-term memory, and conditional random fields. Experiments are carried out on the SemEval-2015 cQA dataset. We attain 58.35% on macroaveraged $\mathrm{F_1}$, which outperforms the Top-1 system in the shared task by 1.16% and improves the state-of-the-art deep-neural-network-based method by 2.21%.
Thermodynamics and NMR of internal G.T mismatches in DNA.
(1997)

Thermodynamics of 39 oligonucleotides with internal G.T mismatches dissolved in 1 M NaCl were determined from UV absorbance versus temperature profiles. These data were combined with literature values of six sequences to derive parameters for 10 linearly independent trimer and tetramer sequences with G.T mismatches and Watson-Crick base pairs. The G.T mismatch parameters predict DeltaG degrees 37, DeltaH degrees , DeltaS degrees , and TM with average deviations of 5.1%, 7.5%, 8.0%, and 1.4 degrees C, respectively. These predictions are within the limits of what can be expected for a nearest-neighbor model. The data show that the contribution of a single G.T mismatch to helix stability is context dependent and ranges from +1.05 kcal/mol for AGA/TTT to -1.05 kcal/mol for CGC/GTG. Several tests of the applicability of the nearest-neighbor model to G.T mismatches are described. Analysis of imino proton chemical shifts show that structural perturbations from the G.T mismatches are highly localized. One-dimensional NOE difference spectra demonstrate that G.T mismatches form stable hydrogen-bonded wobble pairs in diverse contexts. Refined nearest-neighbor parameters for Watson-Crick base pairs are also presented.
Comparing Blockchain and Cloud Services for Business Process Execution
(2017)

Blockchain is of rising importance as a technology for engineering applications in cross-organizational settings, avoiding reliance on central trusted third-parties. The use of blockchain, instead of traditional databases or services, is an architectural choice in the development of a software system. The costs of execution and storage are important non-functional qualities, but as yet very little has been done to study them for blockchain-based systems. We investigate the cost of using blockchain using business process execution as a lens. Specifically, we compare the cost for computation and storage of business process execution on blockchain vs. a popular cloud service. First, we capture the cost models for both alternatives. Second, we implemented and measured the cost of business process execution on blockchain and cloud services for an example business process model from the literature. We observe two orders of magnitude difference in this cost.
Bipartite graph reinforcement model for web image annotation
(2007)

Automatic image annotation is an effective way for managing and retrieving abundant images on the internet. In this paper, a bipartite graph reinforcement model (BGRM) is proposed for web image annotation. Given a web image, a set of candidate annotations is extracted from its surrounding text and other textual information in the hosting web page. As this set is often incomplete, it is extended to include more potentially relevant annotations by searching and mining a large-scale image database. All candidates are modeled as a bipartite graph. Then a reinforcement algorithm is performed on the bipartite graph to re-rank the candidates. Only those with the highest ranking scores are reserved as the final annotations. Experimental results on real web images demonstrate the effectiveness of the proposed model.
Design and prototyping of 3-phase BLDC motor
(2017)

The development of electric vehicle is now growing rapidly. Demands to deliver a reliable and easy to drive in motor control causes Brushless Direct Current (BLDC) motor becomes a potential candidate. A BLDC motor drive is a potential option for an electric vehicle since it has a high reliability, simple design, and ability to work at high rotation per minute (RPM). This paper discussed the Permanent Magnet BLDC Motor design method. The structure of an interior rotor permanent magnet type is selected to be used in the design of Permanent Magnet BLDC motor so that it can be applied in a drive that requires a large torque and capable of acceleration and deceleration with good response. Selection of 12 slots and 8 poles configuration aims for improving the motor performance. The motor is designed and simulated using a software-based Motor Solve FEA (Finite Element Analysis). Based on this design and simulation results, a prototype of BLDC motor is built. Parameters testing as stator resistance, inductances (the d-axis and q-axis inductance), and the back emf constant (Ke) were used to evaluate the result of the design and prototype motor. Measuring the prototype motor's parameters was carried out by several different methods depending on the parameters tested. Stator resistance testing is performed with the measurement of current in the coil which is then obtained by calculating the magnitude of stator resistance as 0.14710296 Ω. Measurements of d-axis stator inductance, q-axis stator inductance, and back emf constant of prototype permanent magnet BLDC motor is obtained as results of 0.35304710 mH, 0.38246769 mH and 0.09690626 Vs/rad respectively. The test results between design and prototype testing were quite good. The difference between the test results and the design of the prototype test results was caused by incompatibility of material composition although using the same type of material. The evaluation shows the electromagnetic parameters is influenced by its constituent materials.
Edge Computing – EDGE 2018
(2018)

Edge computing systems (Cloudlet, Fog Computing, Multi-access Edge Computing) provide numerous benefits to information technology: reduced latency, improved bandwidth, battery lifetime, etc. Despite all the benefits, edge computing systems have several issues that could significantly reduce the performance of certain applications. Indeed, current edge computing technologies do not assure ultra-low latency for real-time applications and they encounter overloading issues for data processing. To solve the aforementioned issues, we propose Home Edge Computing (HEC): a new three-tier edge computing architecture that provides data storage and processing in close proximity to the users. The term “Home” in Home Edge Computing does not restrain our work to the homes of the users, we take into account other places where the users could connect to the Internet such as: companies, shopping malls, hospitals, etc. Our three-tier architecture is composed of a Home Server, an Edge Server and a Central Cloud which we also find in traditional edge computing architectures. The Home Server is located within the vicinities of the users which allow the achievement of ultra-low latency for applications that could be processed by the said server; this also help reduce the amount of data that could be treated in the Edge Server and the Central Cloud. We demonstrate the validity of our architecture by leveraging the EdgeCloudSim simulation platform. The results of the simulation show that our proposal can, in fact, help achieve ultra-low latency and reduce overloading issues.
Network Decoupling: From Regular to Depthwise Separable Convolutions
(2018)

Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depthwise separable convolutions, and proves that the former one could be approximated with the latter one in closed form. We show depthwise separable convolutions are principal components of regular convolutions. And then we propose network decoupling (ND), a training-free method to accelerate convolutional neural networks (CNNs) by transferring pre-trained CNN models into the MobileNet-like depthwise separable convolution structure, with a promising speedup yet negligible accuracy loss. We further verify through experiments that the proposed method is orthogonal to other training-free methods like channel decomposition, spatial decomposition, etc. Combining the proposed method with them will bring even larger CNN speedup. For instance, ND itself achieves about 2× speedup for the widely used VGG16, and combined with other methods, it reaches 3.7× speedup with graceful accuracy degradation. We demonstrate that ND is widely applicable to classification networks like ResNet, and object detection network like SSD300.
Sixty years of fear appeal research: current state of the evidence.
(2014)

Fear arousal is widely used in persuasive campaigns and behavioral change interventions. Yet, experimental evidence argues against the use of threatening health information. The authors reviewed the current state of empirical evidence on the effectiveness of fear appeals. Following a brief overview of the use of fear arousal in health education practice and the structure of effective fear appeals according to two main theoretical frameworks-protection motivation theory and the extended parallel process model-the findings of six meta-analytic studies in the effectiveness of fear appeals are summarized. It is concluded that coping information aimed at increasing perceptions of response effectiveness and especially self-efficacy is more important in promoting protective action than presenting threatening health information aimed at increasing risk perceptions and fear arousal. Alternative behavior change methods than fear appeals should be considered.
The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks
(1996)

In the past three decades, earnings have been one of the most researched variables in accounting. Empirical research provided substantial evidence on its usefulness in the capital markets but evidence in predicting earnings has been limited, yielding inconclusive results. The purpose of this study is to validate and extend prior research in predicting earnings by examining aggregate and industry-specific data. A sample of 10,509 firm-year observations included in the Compustat database for the period 1982-91 is used in the study. The stepwise logistic regression results of the present study indicated that nine earnings and non-earnings variables can be used to predict earnings. These predictor variables are not identical to those reported in prior studies. These results are also extended to the manufacturing industry. Two new variables are identified to be significant in this industry. Moreover, an Artificial Neural Network (ANN) approach is employed to complement the logistic regression results. The ANN model's performance is at least as high as the logistic regression model's predictive ability.
Compact-Size Low-Profile Wideband Circularly Polarized Omnidirectional Patch Antenna With Reconfigurable Polarizations
(2016)

A compact-size low-profile wideband circularly polarized (CP) omnidirectional antenna with reconfigurable polarizations is presented in this communication. This design is based on a low-profile omnidirectional CP antenna which consists of a vertically polarized microstrip patch antenna working in TM01/TM02 modes and sequentially bended slots etched on the ground plane for radiating horizontally polarized electric field. The combined radiation from both the microstrip patch and the slots leads to a CP omnidirectional radiation pattern. The polarization reconfigurability is realized by introducing PIN diodes on the slots. By electronically controlling the states of the PIN diodes, the effective orientation of the slots on ground plane can be changed dynamically and the polarization of antenna can be altered between left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). The proposed antenna exhibits a wide-operational bandwidth of 19.8% (2.09-2.55 GHz) with both axial ratio below 3 dB and return loss above 10 dB when radiates either LHCP or RHCP waves. Experimental results show good agreement with the simulation results. The present design has a compact size, a thickness of only 0.024λ and exhibits stable CP omnidirectional conical-beam radiation patterns within the entire operating frequency band with good circular polarization.
Substrate Integrated Waveguide Loaded by Complementary Split-Ring Resonators and Its Applications to Miniaturized Waveguide Filters
(2009)

A substrate integrated waveguide with square complementary split-ring resonators (CSRRs) etched on the waveguide surface is investigated in this paper. The proposed structures allow the implementation of a forward-wave passband propagating below the characteristic cutoff frequency of the waveguide. By changing the orientations of the CSRRs, which are incorporated in the waveguide surface and can be interpreted in terms of electric dipoles, varied passband characteristics are observed. A detailed explanation for the generation and variations of the passbands has been illuminated. The application of this waveguide and CSRR combination technique to the design of miniaturized waveguide bandpass filters characterized by transmission zeros is then illustrated. Filter design methodology is examined. These proposed filters exhibit high selectivity and compact size due to the employment of the subwavelength resonators and an evanescent-wave transmission. By slightly altering the configuration of the CSRRs, we find that the propagation of the TE10 mode can be suppressed and filters with improved selectivity and stopband rejection can be obtained. To verify the presented concept, three different types of filters are fabricated based on the standard printed circuit board process. The measured results are in good agreement with the simulation.
Head-worn displays: a review
(2006)

Head-worn display design is inherently an interdisciplinary subject fusing optical engineering, optical materials, optical coatings, electronics, manufacturing techniques, user interface design, computer science, human perception, and physiology for assessing these displays. This paper summarizes the state-of-the-art in head-worn display design (HWD) and development. This review is focused on the optical engineering aspects, divided into different sections to explore principles and applications. Building on the guiding fundamentals of optical design and engineering, the principles section includes a summary of microdisplay or laser sources, the Lagrange invariant for understanding the trade-offs in optical design of HWDs, modes of image presentation (i.e., monocular, biocular, and stereo) and operational modes such as optical and video see-through. A brief summary of the human visual system pertinent to the design of HWDs is provided. Two optical design forms, namely, pupil forming and non-pupil forming are discussed. We summarize the results from previous design work using aspheric, diffractive, or holographic elements to achieve compact and lightweight systems. The applications section is organized in terms of field of view requirements and presents a reasonable collection of past designs
Large-Scale Automatic Species Identification
(2017)

The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of ”family”—when applying automatic specificity control.
Health Monitoring and Assistance to Support Aging in Place
(2006)

To many people, home is a sanctuary. For those people who need special medical care, they may need to be pulled out of their home to meet their medical needs. As the population ages, the percentage of people in this group is increasing and the effects are expensive as well as unsatisfying. We hypothesize that many people with disabilities can lead independent lives in their own homes with the aid of athome automated assistance and health monitoring. In order to accomplish this, robust methods must be developed to collect relevant data and process it dynamically and adaptively to detect and/or predict threatening long-term trends or immediate crises. The main objective of this paper is to investigate techniques for using agent-based smart home technologies to provide this at-home health monitoring and assistance. To this end, we have developed novel inhabitant modeling and automation algorithms that provide remote health monitoring for caregivers. Specifically, we address the following technological challenges: 1) identifying lifestyle trends, 2) detecting anomalies in current data, and 3) designing a reminder assistance system. Our solution approaches are being tested in simulation and with volunteers at the UTA’s MavHome site, an agent-based
An Empirical Evaluation of Deep Learning for Network Anomaly Detection
(2018)

Deep learning has been given a great deal of attention with its success story in many areas such as image analysis and speech recognition. In particular, deep learning is good at dealing with high-dimensional data exhibiting non-linearity. Our preliminary study reveals a very high degree of non-linearity from network traffic data, which explains why it is hard to improve the detection accuracy by using conventional machine learning techniques (e.g., SVM, Random Forest, Ad-aboosting). In this study, we empirically evaluate deep learning to see its feasibility for network anomaly detection. We examine a set of deep learning models constructed based on the Fully Connected Network (FCN), Variational AutoEncoder (VAE), and Long Short-Term Memory with Sequence to Sequence (LSTM Seq2Seq) structures, with two public traffic data sets that have distinctive properties with respect to the distribution of normal and attack populations. Our experimental results confirm the potential of deep learning models for network anomaly detection, and the model based on the LSTM Seq2Seq structure shows a highly promising performance, yielding 99% of binary classification accuracy on the public data sets.
A study of speaker adaptation for DNN-based speech synthesis
(2015)

A major advantage of statistical parametric speech synthesis (SPSS) over unit-selection speech synthesis is its adaptability and controllability in changing speaker characteristics and speaking style. Recently, several studies using deep neural networks (DNNs) as acoustic models for SPSS have shown promising results. However, the adaptability of DNNs in SPSS has not been systematically studied. In this paper, we conduct an experimental analysis of speaker adaptation for DNN-based speech synthesis at different levels. In particular, we augment a low-dimensional speaker-specific vector with linguistic features as input to represent speaker identity, perform model adaptation to scale the hidden activation weights, and perform a feature space transformation at the output layer to modify generated acoustic features. We systematically analyse the performance of each individual adaptation technique and that of their combinations. Experimental results confirm the adaptability of the DNN, and listening tests demonstrate that the DNN can achieve significantly better adaptation performance than the hidden Markov model (HMM) baseline in terms of naturalness and speaker similarity.
Noise and Bandwidth analysis for PMOS based wide swing cascode current mirror using 0.35 um CMOS-MEMS technology
(2014)

In this work, we propose a PMOS based wide swing cascode current mirror (WSSCM) for 0.35 um CMOS-MEMS technology. The proposed circuit shows a high bandwidth current mirror with high output resistance capability operating at low voltage levels. Noise, output resistance and bandwidth analysis are presented. CADENCE SPECTRE circuit design tool using MIMOS 0.35 um and BSIM3 transistor models is used to carry out the simulations. The circuit achieves an amplification of 120.04 uA, the input referred noise of the circuit is 75.77 nV/√Hz with a minimum power consumption of 0.33 mW and a high output resistance of 15 MO .
Review of Inflatable Booms for Deployable Space Structures : Packing and Rigidization
(2013)

Inflatable structures offer the potential of compactly stowing lightweight structures, which assume a fully deployed state in space. An important category of space inflatables are cylindrical booms, which may form the structural members of trusses or the support structure for solar sails. Two critical and interdependent aspects of designing inflatable cylindrical booms for space applications are i) packaging methods that enable compact stowage and ensure reliable deployment, and ii) rigidization techniques that provide long-term structural ridigity after deployment. The vast literature in these two fields is summarized to establish the state of the art.